基于图像识别技术的鱼群浮头检测控制系统设计
Design of Detection and Control System for Fish Head Floating Based on Image Recognition Technology
摘要: 智能视频监控随着AI智能技术的发展,可以不需要人工干预就对检测目标进行识别、定位和跟踪操作。发展工业化养鱼离不开智能视频监控与智能管理的支撑,其智能监测的设计使工业化养鱼得到了发展。本文通过投喂平台的高清摄像头视频监控设备,监测并判别鱼群的浮动状态,结合NB-IOT模块和无线网桥,实现对鱼群集群行为、浮头行为进行实时监测的功能,同时在MATLAB的APP设计工具中,以图像识别理论为前提,将高清摄像头采集到的图像,结合图像处理算法,对鱼群图像进行边缘检测、平滑处理、边缘膨胀、数目标记等操作,以此对鱼群是否浮头进行判断,最后根据现有数据预测鱼群的行为走势。
Abstract:
With the development of AI intelligent technology, intelligent video surveillance can identify, locate and track detection targets without manual intervention. The development of industrialized fish farming is inseparable from the support of intelligent video monitoring and intelligent management. Its intelligent monitoring design has enabled the development of industrialized fish farming. This article monitors and discriminates the floating state of fish schools through the HD camera video monitoring equipment of the feeding platform, combined with NB-IOT module and wireless bridge to realize the function of real-time monitoring of fish group behavior and floating head behavior. In the APP design tool, based on the premise of image recognition theory, the images collected by the high-definition camera are combined with image processing algorithms to perform edge detection, smoothing, edge expansion, number marking and other operations on the fish image to determine whether the fish are The floating head judges, and finally predicts the behavior of the fish school based on the existing data.
参考文献
|
[1]
|
王亚楠. 鱼浮头如何预防与应对[J]. 广东饲料, 2016, 25(1): 49-52.
|
|
[2]
|
黎志伟, 祝素玉. 引起鱼浮头的10种原因及对策(下) [J]. 当代水产, 2017, 42(1): 86.
|
|
[3]
|
黎志伟, 祝素玉. 引起鱼浮头的10种原因及对策(上) [J]. 当代水产, 2016, 41(12): 74.
|
|
[4]
|
朱云生. 鱼浮头、泛塘起因不同防控对策也各异[J]. 渔业致富指南, 2020(6): 57-61.
|
|
[5]
|
韦丹. 水质在线监测系统在污水处理中的应用[J]. 中国资源综合利用, 2020, 38(5): 178-180.
|
|
[6]
|
江雪芸, 杜卫民. 基于NB-IOT技术的智能停车解决方案初探[J]. 网络安全技术与应用, 2020(6): 128-129.
|
|
[7]
|
纪明明, 刘礼文, 陈明武, 等. 窄带物联网在智慧校园建设中的应用探索[J]. 产业与科技论坛, 2022, 21(5): 58-59.
|
|
[8]
|
陶锋, 夏玉龙, 崔文露, 等. 基于无线网桥的鄂北调水工程视频监控系统建设[J]. 人民长江, 2020, 51(3): 213-217.
|
|
[9]
|
陈海峰, 丁丽丽. 二值化图像的灰度处理算法研究[J]. 电脑与电信, 2019(7): 34-38.
|
|
[10]
|
赵良军, 董林鹭, 杨平先, 等. 采用先验知识的边缘提取算法[J]. 四川师范大学学报(自然科学版), 2022, 45(1): 136-142.
|
|
[11]
|
Wang, J., Li, Y., Choi, Y., Lee, C. and Kim, J. (2020) Fast and Accurate Smoothing Method Using a Modified Allen-Cahn Equation. Computer-Aided Design, 120, 102804. [Google Scholar] [CrossRef]
|
|
[12]
|
杜文汉, 李东兴, 王倩楠, 等. 融合改进帧差和边缘提取算法的运动目标检测[J]. 科学技术与工程, 2022, 22(5): 1944-1949.
|